A weighted mean temperature (T_m) augmentation method based on global latitude zone

Abstract

The weighted mean temperature (T_m) is a function of atmospheric temperature and vertical humidity profiles. It plays a crucial role in the progress of retrieving water vapor information from the tropospheric delay of GNSS signals. The T_m estimated by the empirical models is always used to convert the zenith wet delay (ZWD) to precipitable water vapor (PWV) in GNSS meteorology. However, these empirical T_m models used trigonometric functions, making it difficult to describe T_m in detail and leading to an obvious accuracy difference with latitude changes. Thus, a global latitude zone augmentation mode was adopted for the empirical T_m models; the augmentation coefficients for each latitude zone were obtained by introducing the measured surface temperature and using the least-squares method. Using the T_m data of 2011–2015 derived from radiosonde, the GPT3 model, UNB3m model, and GWTMD model were augmented and analyzed. The results show that all augmentation models can improve the accuracy of the estimated T_m compared with their corresponding original models, and their levels of improvement are different. The three augmentation models achieved an average RMSE of 2.79 K, 3.47 K, and 3.22 K, which correspond to 22%, 49%, and 8% improvement against the GPT3 model, UNB3m model, and GWTMD model. In addition, the comparisons with the T_m linear formula were carried out and showed the superiority of the augmentation models.